Average Reward Dynamic Programming
Average Reward Dynamic Programming is a dynamic programming method that specifically addresses infinite-horizon Markov decision processes by optimizing the long-run average reward per time step, distinguishing it from alternative criteria such as discounted or total reward formulations.
52
Publications
2.8K
Citations
88
Authors
64
Institutions
Publications per year
2017–2026
8
Parents
52
Solving Semi-Markov Decision Problems Using Average Reward Reinforcement Learning
Tapas K. Das, Abhijit Gosavi, Sridhar Mahadevan et al. · Management Science · 1999 · 220 citations
On Dynamic Programming with Unbounded Rewards
Steven A. Lippman · Management Science · 1975 · 167 citations
1–5 of 52
88
Massachusetts Institute of Technology, United States
4 publications · 497 citations
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4
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3
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Missouri University of Science and Technology, United States
3 publications · 340 citations
3
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3
h-index
1–5 of 88
64
Massachusetts Institute of Technology
Cambridge, United States
5 h-index
7 publications
671 citations
University of Cambridge
Cambridge, United Kingdom
3 h-index
3 publications
468 citations

University of Massachusetts Amherst
Amherst Center, United States
3 h-index
4 publications
99 citations
University of South Florida
Tampa, United States
3 h-index
5 publications
889 citations
California Southern University
Chandler, United States
3 h-index
7 publications
195 citations
University of Southern California
Los Angeles, United States
3 h-index
7 publications
195 citations
University at Buffalo, State University of New York
Buffalo, United States
2 h-index
2 publications
120 citations
California University of Pennsylvania
California, United States
2 h-index
2 publications
93 citations
1–8 of 64
3 publications · 518 citations
3
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3 publications · 359 citations
3
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Repository · 2 publications · 65 citations
2
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2 publications · 59 citations
2
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2 publications · 28 citations
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